The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning...

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Detalles Bibliográficos
Autores principales: Kamrud, Alexander, Borghetti, Brett, Schubert Kabban, Christine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125354/
https://www.ncbi.nlm.nih.gov/pubmed/34066595
http://dx.doi.org/10.3390/s21093225

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125354/
https://www.ncbi.nlm.nih.gov/pubmed/34066595
http://dx.doi.org/10.3390/s21093225

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